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INTRODUCTION

A key feature of human intelligence is our ability to categorise and summarise large quantities of data, whether this data arises from sensory input or from other sources. The ability to group multiple entities together into an (approximately) uniform whole allows us to efficiently represent a whole group as a single concept, enabling us to reason, and to derive knowledge, about groups of entities. A simple form of derived knowledge is association - essentially, that the extensions of two concepts overlap significantly. One of the fundamental tenets underlying fuzzy set theory (Zadeh, 1965) is the idea that humans work with groups of entities (or conceptual categories) that are loosely defined, able to admit elements according to some scale of membership rather than according to an absolute yes/no test. This is particularly true where the knowledge and/or reasoning uses natural language - humans can communicate quickly and efficiently with an informal shared understanding of the vocabulary. Although different individuals may have slightly different interpretations of terms, meaning can still be conveyed sufficiently accurately in almost all cases.

A further step in the idea of grouping entities together leads us to the notion of a taxonomy, i.e. a hierarchical series of progressively more refined categories. This enables us to represent / reason about problems at the appropriate level of granularity, and the use of taxonomic hierarchies to organise information and sets of objects into manageable chunks (granules) is widespread. For example, taxonomies serve as the main organisational principle for the grouping of species, for systems of government (national - regional - local), for corporate and command structures, for libraries, for document repositories and very many other applications.


  

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